DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-17, 22-25 and 30-33 are rejected under 35 U.S.C. 103 as being unpatentable over Jie et al (CN 112927244, an IDS document) in view of Liu et al (US Pub. 2023/0260247).
With respect to claim 14, Jie discloses A model training method, comprising: obtaining a sample image; obtaining an initial label of the sample image, wherein the initial label comprises a the sample image is labeled with one target point, and the target point labeled by the object is a point in the object; generating an extended label of the sample image based on the initial label, wherein the extended label comprises a semantic category and an instance category to which an association point of the target point belongs, and the semantic category and the instance category to which the association point belongs are determined based on a semantic category and an instance category to which the target point belongs; and training an initial neural network model based on the initial label and the extended label, and determining the neural network model at the end of the training as an instance segmentation model, (see description for figure 1, wherein … step 101, marking a point [is read as “the target point labeled”] on each instance to give out the instance position and semantic type, using the over-segmentation algorithm to expand the marking point to the over-divided block, obtaining the block-level tag of each instance. step 102, according to the adjacent relation between the over-divided block, each over-divided block is a node constructing over-divided block diagram; the marked condition of each over-divided block is expanded to the corresponding image node in the over-divided block diagram [is read as “extended label comprises a semantic category and an instance category to which an association point of the target point belongs”]. step 104, in the divided block diagram after executing the step of the node respectively to different instances in the three-dimensional scene, node feature corresponding to the instance feature, by inputting the instance feature input classifier network to obtain the classification score of the marked semantic category; calculating loss and using gradient reverse transmission training combined network and classifier network. step 105, after the combined network and classifier network training, inputting the combined network input three-dimensional point cloud scene and marked block level label, the pseudo label output by the combined network as the monitoring information on the training set training the existing strong monitoring point cloud scene dividing network; after training, performing performance evaluation to the strong monitoring point cloud scene dividing network on the test set.), as claimed.
However, Jie fails to disclose a semantic category and an instance category, as claimed.
Liu teaches a semantic category and an instance category, (see paragraph 0044 wherein … panoptic segmentation may provide class information for every pixel in the scene using semantic segmentation, as well detect and count the number of objects for every “thing” (e.g., instance class) in the scene using instance segmentation), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of segmentation using image analysis. Teaching of Liu to have semantic category and instance category for each point can be incorporated into the Jie system as suggested (see description for figure I step 104), for suggestion, and modifying the system yields panoptic segmentation (see paragraph 0002 of Liu), for motivation.
With respect to claim 15, combination of Jie and Liu further discloses wherein generating the extended label of the sample image based on the initial label comprises: obtaining a low-level feature of the sample image; performing a segmentation processing on the sample image according to the low-level feature, and obtaining the association point of the target point based on a segmentation result; and obtaining the extended label of the sample image by determining the semantic category and the instance category to which the target point belongs as the semantic category and the instance category to which the association point belongs to, (see Jie description for figure 1., wherein …step 104, in the divided block diagram after executing the step of the node respectively to different instances in the three-dimensional scene, node feature corresponding to the instance feature, by inputting the instance feature input classifier network to obtain the classification score of the marked semantic category; calculating loss and using gradient reverse transmission training combined network and classifier network), as claimed.
With respect to claim 16, combination of Jie and Liu further discloses wherein performing the segmentation processing on the sample image according to the low-level feature, and obtaining the association point of the target point based on the segmentation result comprises at least one of the following: in response to that the sample image is a two-dimensional image, performing a superpixel segmentation processing on the sample image according to the low-level feature, and obtaining an association point of the target point based on a segmentation result; and in response to that the sample image is a three-dimensional image, performing a supervoxel segmentation processing on the sample image according to the low-level feature, and obtaining an association point of the target point based on a segmentation result, (see Liu paragraph 0056, wherein …In brief overview, as will be described in more detail below with reference to FIGS. 3A to 3D, the preprocessor/encoder 202 may extract various line features and initial features (e.g., edges, horizontal lines, vertical lines, curved lines, shapes, and the like) from a two-dimensional (2D) image or three-dimensional (3D) point cloud (which may be collectively referred to hereinafter as an input image or an input scene) to generate an initial feature map, and may provide the initial feature map to each of the semantic segmentation branch and the instance segmentation branch…), as claimed.
With respect to claim 17, combination of Jie and Liu further discloses wherein training a preset neural network model based on the initial label and the extended label, and taking the neural network model at the end of the training as the instance segmentation model comprises: performing a first training on the initial neural network model based on the initial label and the extended label, and determining the neural network model that reaches a first training end condition as a first neural network model; obtaining an output result of the first neural network model for each to-be-measured point in the sample image, and generating a pseudo label based on the output result, the pseudo label comprising a semantic category and an instance category to which each of the to-be-measured points in the sample image belongs; and performing a second training on the first neural network model based on the pseudo label, and determining the first neural network model that reaches a second training end condition as an instance segmentation model, (see Jie description for figure 1, wherein…step 105, after the combined network and classifier network training, inputting the combined network input three-dimensional point cloud scene and marked block level label, the pseudo label output by the combined network as the monitoring information on the training set training the existing strong monitoring point cloud scene dividing network; after training, performing performance evaluation to the strong monitoring point cloud scene dividing network on the test set), as claimed.
Claims 22-25 and 30-33 are rejected for the same reasons as set forth in the rejections for claims 14-17, because claims 22-25 and 30-33 are claiming subject matter of similar scope as claimed in claims 14-17. Furthermore, see Liu figure 2 for the model as claimed in claim 22.
Allowable Subject Matter
Claims 18-21 and 26-29 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIKKRAM BALI whose telephone number is (571)272-7415. The examiner can normally be reached Monday-Friday 7:00AM-3:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VIKKRAM BALI/Primary Examiner, Art Unit 2663
Prosecution Insights